Method and apparatus for allocating downlink resources in a multiple-input multiple-output (MIMO) communication system

ABSTRACT

Techniques to schedule downlink data transmission to a number of terminals in a wireless communication system. In one method, one or more sets of terminals are formed for possible data transmission, with each set including a unique combination of one or more terminals and corresponding to a hypothesis to be evaluated. One or more sub-hypotheses may further be formed for each hypothesis, with each sub-hypothesis corresponding to specific assignments of a number of transmit antennas to the one or more terminals in the hypothesis. The performance of each sub-hypothesis is then evaluated, and one of the evaluated sub-hypotheses is selected based on their performance. The terminal(s) in the selected sub-hypothesis are then scheduled for data transmission, and data is thereafter coded, modulated, and transmitted to each scheduled terminal from one or more transmit antennas assigned to the terminal.

CLAIM OF PRIORITY UNDER 35 U.S.C. §120

The present application for patent is a Continuation and claims priorityto patent application Ser. No. 09/859,345 entitled “METHOD AND APPARATUSFOR ALL ALLOCATING DOWNLINK RESOURCES IN A MULTIPLE-INPUTMULTIPLE-OUTPUT (MIMO) COMMUNICATION SYSTEM” filed May 16, 2001 now U.S.Pat. No. 6,662,024, and assigned to the assignee hereof and herebyexpressly incorporated by reference herein.

BACKGROUND

1. Field

The present invention relates generally to data communication, and morespecifically to techniques for allocating downlink resources in amultiple-input multiple-output (MIMO) communication system.

2. Background

Wireless communication systems are widely deployed to provide varioustypes of communication such as voice, data, and so on, for a number ofusers. These systems may be based on code division multiple access(CDMA), time division multiple access (TDMA), frequency divisionmultiple access (FDMA), or some other multiple access techniques.

A multiple-input multiple-output (MIMO) communication system employsmultiple (N_(T)) transmit antennas and multiple (N_(R)) receive antennasfor transmission of multiple independent data streams. In one commonMIMO system implementation, the data streams are transmitted to a singleterminal at any given time. However, a multiple access communicationsystem having a base station with multiple antennas may alsoconcurrently communicate with a number of terminals. In this case, thebase station employs a number of antennas and each terminal employsN_(R) antennas to receive one or more of the multiple data streams.

The connection between a multiple-antenna base station and a singlemultiple-antenna terminal is called a MIMO channel. A MIMO channelformed by these N_(T) transmit and N_(R) receive antennas may bedecomposed into N_(C) independent channels, with N_(C)≦min {N_(T),N_(R)}. Each of the N_(C) independent channels is also referred to as aspatial subchannel of the MIMO channel and corresponds to a dimension.The MIMO system can provide improved performance (e.g., increasedtransmission capacity) if the additional dimensionalities of thesesubchannels created by the multiple transmit and receive antennas areutilized.

Each MIMO channel between the base station and a terminal typicallyexperiences different link characteristics and is associated withdifferent transmission capability, so the spatial subchannels availableto each terminal have different effective capacities. Efficient use ofthe available downlink resources (and higher throughput) may be achievedif the N_(C) available spatial subchannels are effectively allocatedsuch that data is transmitted on these subchannels to a “proper” set ofterminals in the MIMO system.

There is therefore a need in the art for techniques to allocate downlinkresources in a MIMO system to provide improved system performance.

SUMMARY

Aspects of the invention provide techniques to increase the downlinkperformance of a wireless communication system. In an aspect, data maybe transmitted from a base station to one or more terminals using one ofa number of different operating modes. In a MIMO mode, all availabledownlink data streams are allocated to a single terminal that employsmultiple antennas (i.e., a MIMO terminal). In an N-SIMO mode, a singledata stream is allocated to each of a number of distinct terminals, witheach terminal employing multiple antennas (i.e., SIMO terminals). And ina mixed-mode, the downlink resources may be allocated to a combinationof SIMO and MIMO terminals, with both types of terminals beingsimultaneously supported. By transmitting data simultaneously tomultiple SIMO terminals, one or more MIMO terminals, or a combinationthereof, the transmission capacity of the system is increased.

In another aspect, scheduling schemes are provided to schedule datatransmissions to active terminals. A scheduler selects the bestoperating mode to use based on various factors such as, for example, theservices being requested by the terminals. In addition, the schedulercan perform an additional level of optimization by selecting aparticular set of terminals for simultaneous data transmission andassigning the available transmit antennas to the selected terminals suchthat high system performance and other requirements are achieved.Several scheduling schemes and antenna assignment schemes are providedand described below.

A specific embodiment of the invention provides a method for schedulingdownlink data transmission to a number of terminals in a wirelesscommunication system. In accordance with the method, one or more sets ofterminals are formed for possible data transmission, with each setincluding a unique combination of one or more terminals andcorresponding to a hypothesis to be evaluated. One or moresub-hypotheses may further be formed for each hypothesis, with eachsub-hypothesis corresponding to specific assignments of a number oftransmit antennas to the one or more terminals in the hypothesis. Theperformance of each sub-hypothesis is then evaluated, and one of theevaluated sub-hypotheses is selected based on their performance. Theterminal(s) in the selected sub-hypothesis are then scheduled for datatransmission, and data is thereafter transmitted to each scheduledterminal from one or more transmit antennas assigned to the terminal.

Each transmit antenna may be used to transmit an independent datastream. To achieve high performance, each data stream may be coded andmodulated based on a scheme selected, for example, based on asignal-to-noise-plus-interference (SNR) estimate for the antenna used totransmit the data stream.

Terminals desiring data transmission (i.e., “active” terminals) may beprioritized based on various metrics and factors. The priority of theactive terminals may then be used to select which terminal(s) to beconsidered for scheduling and/or to assign the available transmitantennas to the selected terminals.

The invention further provides methods, systems, and apparatus thatimplement various aspects, embodiments, and features of the invention,as described in further detail below.

BRIEF DESCRIPTION OF THE DRAWINGS

The features, nature, and advantages of the present invention willbecome more apparent from the detailed description set forth below whentaken in conjunction with the drawings in which like referencecharacters identify correspondingly throughout and wherein:

FIG. 1 is a diagram of a multiple-input multiple-output (MIMO)communication system that may be designed and operated to implementvarious aspects and embodiments of the invention;

FIG. 2 is a flow diagram of a process to schedule terminals for datatransmission, in accordance with an embodiment of the invention;

FIG. 3 is a flow diagram of a process to assign transmit antennas toreceive antennas using a “max—max” criterion, in accordance with anembodiment of the invention;

FIG. 4 is a flow diagram for a priority-based scheduling scheme wherebya set of one or more highest priority terminals is considered forscheduling, in accordance with an embodiment of the invention;

FIG. 5 is a block diagram of a base station and a number of terminals inthe MIMO communication system;

FIG. 6 is a block diagram of an embodiment of the transmit portion of abase station capable of processing data for transmission to theterminals based on the available CSI;

FIG. 7 is a block diagram of an embodiment of the receive portion of aterminal;

FIGS. 8A and 8B are block diagrams of an embodiment of a channelMIMO/data processor and an interference canceller, respectively, of areceive (RX) MIMO/data processor at the terminal; and

FIG. 9 shows the average throughput for a MIMO communication system withfour transmit antennas (i.e., N_(T)=4) and four receive antennas at eachterminal (i.e., N_(R)=4) for two different operating modes.

DETAILED DESCRIPTION

FIG. 1 is a diagram of a multiple-input multiple-output (MIMO)communication system 100 that may be designed and operated to implementvarious aspects and embodiments of the invention. MIMO system 100employs multiple (N_(T)) transmit antennas and multiple (N_(R)) receiveantennas for data transmission. MIMO system 100 is effectively formedfor a multiple access communication system having a base station (BS)104 that can concurrently communicate with a number of terminals (T)106. In this case, base station 104 employs multiple antennas andrepresents the multiple-input (MI) for downlink transmissions from thebase station to the terminals.

A set of one or more “communicating” terminals 106 collectivelyrepresents the multiple-output (MO) for downlink transmissions. As usedherein, a communicating terminal is one that receives user-specific datafrom the base station, and an “active” terminal is one that desires datatransmission in an upcoming or future transmission interval. Activeterminals may include terminals that are currently communicating.

MIMO system 100 may be designed to implement any number of standards anddesigns for CDMA, TDMA, FDMA, and other multiple access techniques. TheCDMA standards include the IS-95, cdma2000, and W-CDMA standards, andthe TDMA standards include the Global System for Mobile Communications(GSM) standard. These standards are known in the art and incorporatedherein by reference.

MIMO system 100 may be operated to transmit data via a number oftransmission channels. Each terminal 106 communicates with base station104 via a MIMO channel. A MIMO channel may be decomposed into N_(C)independent channels, with N_(C)≦min {N_(T), N_(R)}. Each of the N_(C)independent channels is also referred to as a spatial subchannel of theMIMO channel. For a MIMO system not utilizing orthogonal frequencydivision modulation (OFDM), there is typically only one frequencysubchannel and each spatial subchannel may be referred to as a“transmission channel”. And for a MIMO system utilizing OFDM, eachspatial subchannel of each frequency subchannel may be referred to as atransmission channel.

For the example shown in FIG. 1, base station 104 concurrentlycommunicates with terminals 106 a through 106 d (as indicated by thesolid lines) via multiple antennas available at the base station andmultiple antennas available at each terminal. Terminals 106 e through106 h may receive pilot references and other signaling information frombase station 104 (as indicated by the dashed lines), but are notreceiving user-specific data from the base station.

Each terminal 106 in MIMO system 100 employs N_(R) antennas forreception of one or more data streams. Generally, the number of antennasat each terminal is equal to or greater than the number of data streamstransmitted by the base station. However, the terminals in the systemneed not all be equipped with equal number of receive antennas.

For MIMO system 100, the number of antennas at each of the terminals(N_(R)) is typically greater than or equal to the number of antennas atthe base station (N_(T)). In this case, for the downlink, the number ofspatial subchannels is limited by the number of transmit antennas at thebase station. Each transmit antenna may be used to send an independentdata stream that may be coded and modulated based on a scheme supportedby the spatial subchannel associated with the MIMO channel between thebase station and the selected terminal.

Aspects of the invention provide techniques to increase the performanceof a wireless communication system. These techniques may beadvantageously used to increase the downlink capacity of a multipleaccess cellular system. These techniques may also be used in combinationwith other multiple access techniques.

In an aspect, data may be transmitted from a base station to one or moreterminals using one of a number of different operating modes. In a MIMOmode, the available downlink resources are allocated to a singleterminal (i.e., a MIMO terminal). In an N-SIMO mode, the downlinkresources are allocated to a number of distinct terminals, with eachterminal demodulating a single data stream (i.e., SIMO terminals). Andin a mixed-mode, the downlink resources may be allocated to acombination of SIMO and MIMO terminals, with both types of terminalsbeing simultaneously supported on the same channel, which may be a timeslot, a code channel, a frequency subchannel, and so on. By transmittingdata simultaneously to multiple SIMO terminals, one or more MIMOterminals, or a combination thereof, the transmission capacity of thesystem is increased.

In another aspect, scheduling schemes are provided to schedule datatransmissions to active terminals. A scheduler selects the bestoperating mode to use based on various factors such as, for example, theservices being requested by the terminals. In addition, the schedulercan perform an additional level of optimization by selecting aparticular set of terminals for simultaneous data transmission andassigning the available transmit antennas to the selected terminals suchthat high system performance and other requirements are achieved.Several scheduling schemes and antenna assignment schemes are describedin further detail below.

With MIMO, multiple independent data streams may be transmitted from thebase station via multiple transmit antennas to one or more scheduledterminals. If the propagation environment has sufficient scattering,MIMO receiver processing techniques may be used at the terminals toefficiently exploit the spatial dimensionalities of the MIMO channel toincrease transmission capacity. MIMO receiver processing techniques maybe used when the base station is communicating with multiple terminalssimultaneously. From the terminal's perspective, the same receiverprocessing techniques may be used to process N_(T) different signalsintended for that terminal (e.g., a single MIMO terminal) or just one ofthe N_(T) signals (i.e., SIMO terminals).

As shown in FIG. 1, the terminals may be randomly distributed in thebase station's coverage area (or “cell”) or may be co-located. For awireless communication system, the link characteristics typically varyover time due to a number of factors such as fading and multipath. At aparticular instant in time, the channel response between the basestation's array of N_(T) transmit antennas and the N_(R) receiveantennas for a single terminal may be characterized by a matrix H whoseelements are composed of independent Gaussian random variables, asfollows:

$\begin{matrix}{H = {\left\lbrack {{\underset{\_}{h}}_{1}{\underset{\_}{h}}_{2}\mspace{14mu}\ldots\mspace{14mu}{\underset{\_}{h}}_{N_{T}}} \right\rbrack = {\begin{bmatrix}h_{1,1} & h_{2,1} & \ldots & h_{N_{T},1} \\h_{1,2} & h_{2,2} & \ldots & h_{N_{T},2} \\\vdots & \vdots & \; & \vdots \\h_{1,N_{R}} & h_{2,N_{R}} & \ldots & h_{N_{T},N_{R}}\end{bmatrix}.}}} & {{Eq}\mspace{14mu}(1)}\end{matrix}$where H is the channel response matrix for the terminal, and h_(i,j) isthe coupling between the base station's i-th transmit antenna and theterminal's j-th receive antenna.

As shown in equation (1), the channel estimates for each terminal may berepresented with a matrix having N_(T)×N_(R) elements corresponding tothe number of transmit antennas at the base station and the number ofreceive antennas at the terminal. Each element of the matrix H describesthe response for a respective transmit-receive antenna pair between thebase station and one terminal. For simplicity, equation (1) describes achannel characterization based on a flat fading channel model (i.e., onecomplex value for the entire system bandwidth). In an actual operatingenvironment, the channel may be frequency selective (i.e., the channelresponse varies across the system bandwidth) and a more detailed channelcharacterization may be used (e.g., each element of the matrix H mayinclude a set of values for different frequency subchannels or timedelays).

The active terminals in the MIMO system periodically estimate thechannel response for each transmit-receive antenna pair. The channelestimates may be facilitated in a number of ways such as, for example,with the use of pilot and/or data decision directed techniques known inthe art. The channel estimates may comprise the complex-value channelresponse estimate for each transmit-receive antenna pair, as describedabove in equation (1). The channel estimates give information on thetransmission characteristics of each of the spatial subchannels, i.e.,what data rate is supportable on each subchannel with a given set oftransmission parameters. The information given by the channel estimatesmay be distilled into a post-processed signal-to-noise-plus-interferenceratio (SNR) estimate for each spatial subchannel (described below), orsome other statistic that allows the transmitter to select the propertransmission parameters for that spatial subchannel. Typically, thisprocess of derivation of the essential statistic reduces the amount ofdata required to characterize a channel. In either case, thisinformation represents one form of channel state information (CSI) thatmay be reported to the base station. Other forms of CSI may also bereported and are described below.

The aggregate CSI received from the collection of terminals may be usedto (1) select a “best” set of one or more terminals for datatransmission, (2) assign the available transmit antennas to the selectedterminals in the set, and (3) select the proper coding and modulationscheme for each transmit antenna. With the available CSI, variousscheduling schemes may be designed to maximize the downlink performanceby evaluating which specific combination of terminals and antennaassignments provide the best system performance (e.g., the highestthroughput) subject to any system constraints and requirements. Byexploiting the spatial (and possibly frequency) “signatures” of theindividual active terminals (i.e., their channel estimates), the averagedownlink throughput can be increased.

The terminals may be scheduled for data transmission based on variousfactors. One set of factors may relate to system constraints andrequirements such as the desired quality of service (QoS), maximumlatency, average data rate, and so on. Some or all of these factors mayneed to be satisfied on a per terminal basis (i.e., for each terminal)in a multiple access communication system. Another set of factors mayrelate to system performance, which may be quantified by an averagesystem throughput rate or some other indications of performance. Thesevarious factors are described in further detail below.

The scheduling schemes can be designed to select the best set ofterminals for simultaneous data transmission on the availabletransmission channels such that system performance is maximized whileconforming to the system constraints and requirements. For simplicity,various aspects of the invention are described below for a MIMO systemwithout OFDM in which one independent data stream may be transmitted bythe base station from each transmit antenna. In this case, (up to) N_(T)independent data streams may be simultaneously transmitted by the basestation from N_(T) transmit antennas and targeted to one or moreterminals, each equipped with N_(R) receive antennas (i.e., N_(T)×N_(R)MIMO), where N_(R)≧N_(T).

For simplicity, the number of receive antennas is assumed to be equal tothe number of transmit antennas (i.e., N_(R)=N_(T)) for much of thedescription below. This is not a necessary condition since all of theanalysis applies for the case where N_(R)≧N_(T).

Scheduling of data transmission on the downlink comprises two parts: (1)selection of one or more sets of terminals for evaluation, and (2)assignment of the available transmit antennas to the terminals in eachset. All or only a subset of the active terminals may be considered forscheduling, and these terminals may be combined to form one or more sets(i.e., hypotheses) to be evaluated. For each hypothesis, the availabletransmit antennas can be assigned to the terminals in the hypothesisbased on any one of a number of antenna assignment schemes. Theterminals in the best hypothesis may then be scheduled for datatransmission in an upcoming interval. The flexibility in both selectingthe best set of terminals for data transmission and the assignment ofthe transmitted antennas to the selected terminals allows the schedulerto optimize performance by exploiting multi-user diversity environment.

In order to determine the “optimum” transmission to a set of terminals,the SNRs or some other sufficient statistics are provided for eachterminal and each spatial subchannel. If the statistic is the SNR, thenfor each set of terminals to be evaluated for data transmission in anupcoming transmission interval, a hypothesis matrix Γ of“post-processed” SNRs (defined below) for this terminal set may beexpressed as:

$\begin{matrix}{{\Gamma = \begin{bmatrix}\gamma_{1,1} & \gamma_{2,1} & \ldots & \gamma_{N_{T},1} \\\gamma_{1,2} & \gamma_{2,2} & \ldots & \gamma_{N_{T},2} \\\vdots & \vdots & \; & \vdots \\\gamma_{1,N_{T}} & \gamma_{2,N_{T}} & \ldots & \gamma_{N_{T},N_{T}}\end{bmatrix}},} & {{Eq}\mspace{14mu}(2)}\end{matrix}$where γ_(i,j) is the post-processed SNR for a data stream(hypothetically) transmitted from the i-th transmit antenna to the j-thterminal.

In the N-SIMO mode, the N_(T) rows in the hypothesis matrix Γ correspondto N_(T) vectors of SNRs from N_(T) different terminals. In this mode,each row in the hypothesis matrix Γ gives the SNR of each transmit datastream for one terminal. And in the mixed-mode, for a particular MIMOterminal designated to receive two or more data streams, that terminal'svector of SNRs may be replicated such that the vector appears in as manyrows as the number of data streams to be transmitted for the terminal(i.e., one row per data stream). Alternatively, one row in thehypothesis matrix Γ may be used for each SIMO or MIMO terminal, and thescheduler may be designed to mark and evaluate these different types ofterminals accordingly.

At each terminal in the set to be evaluated, the N_(T) (hypothetically)transmitted data streams are received by the terminal's N_(R) receiveantennas, and the N_(R) received signals can be processed using spatialor space-time equalization to separate out the N_(T) transmitted datastreams, as described below. The SNR of a post-processed data stream(i.e., after equalization) may be estimated and comprises thepost-processed SNR for that data stream. For each terminal, a set ofN_(T) post-processed SNRs may be provided for the N_(T) data streamsthat may be received by that terminal.

If a successive equalization and interference cancellation (or“successive cancellation”) receiver processing technique is used at aterminal to process the received signals, then the post-processed SNRachieved at the terminal for each transmitted data stream depends on theorder in which the transmitted data streams are detected (i.e.,demodulated and decoded) to recover the transmitted data, as describedbelow. In this case, a number of sets of SNRs may be provided for eachterminal for a number of possible detection orderings. Multiplehypothesis matrices may then be formed and evaluated to determine whichspecific combination of terminals and detection ordering provides thebest system performance.

In any case, each hypothesis matrix Γ includes the post-processed SNRsfor a specific set of terminals (i.e., hypothesis) to be evaluated.These post-processed SNRs represent the SNRs achievable by the terminalsand are used to evaluate the hypothesis.

FIG. 2 is a flow diagram of a process 200 to schedule terminals for datatransmission, in accordance with an embodiment of the invention. Forclarity, the overall process is first described and the details for someof the steps in the process are described subsequently.

Initially, metrics to be used to select the best set of terminals fordata transmission are initialized, at step 212. Various performancemetrics may be used to evaluate the terminal sets and some of these aredescribed in further detail below. For example, a performance metricthat maximizes system throughput may be used.

A (new) set of one or more active terminals is then selected from amongall active terminals considered for scheduling, at step 214. This set ofterminals forms a hypothesis to be evaluated. Various techniques may beused to limit the number of active terminals to be considered forscheduling, which then reduces the number of hypotheses to be evaluated,as described below. For each terminal in the hypothesis, the SNR vector(e.g., γ _(j)=[γ_(1,j), γ_(2,j), . . . γ_(N) _(T) _(,j)]) is retrieved,at step 216. The SNR vectors for all terminals in the hypothesis formthe hypothesis matrix Γ shown in equation (2).

For each hypothesis matrix Γ of N_(T) transmit antennas and N_(T)terminals, there are N_(T) factorial possible combinations ofassignments of transmit antennas to terminals (i.e., N_(T)!sub-hypotheses). Thus, a particular (new) combination ofantenna/terminal assignments is selected for evaluation, at step 218.This particular combination of antenna/terminal assignments forms asub-hypothesis to be evaluated.

The sub-hypothesis is then evaluated and the performance metric (e.g.,the system throughput) corresponding to this sub-hypothesis isdetermined (e.g., based on the SNRs for the sub-hypothesis), at step220. This performance metric is then used to update the performancemetric corresponding to the current best sub-hypothesis, at step 222.Specifically, if the performance metric for this sub-hypothesis isbetter than that of the current best sub-hypothesis, then thissub-hypothesis becomes the new best sub-hypothesis, and the performancemetric and other terminal metrics corresponding to this sub-hypothesisare saved. The performance and terminal metrics are described below.

A determination is then made whether or not all sub-hypotheses for thecurrent hypothesis have been evaluated, at step 224. If allsub-hypotheses have not been evaluated, the process returns to step 218and a different and not yet evaluated combination of antenna/terminalassignments is selected for evaluation. Steps 218 through 224 arerepeated for each sub-hypothesis to be evaluated.

If all sub-hypotheses for a particular hypothesis have been evaluated,at step 224, a determination is then made whether or not all hypotheseshave been considered, at step 226. If all hypotheses have not beenconsidered, then the process returns to step 214 and a different and notyet considered set of terminals is selected for evaluation. Steps 214through 226 are repeated for each hypothesis to be considered.

If all hypotheses have been considered at step 226, then the specificset of terminals scheduled for data transmission in the upcomingtransmission interval and their assigned transmit antennas are known.The post-processed SNRs corresponding to this set of terminals andantenna assignments may be used to select the proper coding andmodulation schemes for the data streams to be transmitted to theterminals. The scheduled transmission interval, antenna assignments,coding and modulation schemes, other information, or any combinationthereof, may be conveyed to the scheduled terminals (e.g., via a controlchannel), at step 228. Alternatively, the terminals may perform “blind”detection and attempt to detect all transmitted data streams todetermine which ones, if any, of the data streams are intended for them.

If the scheduling scheme requires other system and terminal metrics tobe maintained (e.g. the average data rate over the past K transmissionintervals, latency for data transmission, and so on), then these metricsare updated, at step 230. The terminal metrics may be used to evaluatethe performance of the individual terminals, and are described below.The scheduling is typically performed for each transmission interval.

For a given hypothesis matrix Γ, the scheduler evaluates variouscombinations of transmit antenna and terminal pairings (i.e.,sub-hypotheses) to determine the best assignments for the hypothesis.Various assignment schemes may be used to assign transmit antennas tothe terminals to achieve various system goals such as fairness, maximizeperformance, and so on.

In one antenna assignment scheme, all possible sub-hypotheses areevaluated based on a particular performance metric and thesub-hypothesis with the best performance metric is selected. For eachhypothesis matrix Γ, there are N_(T) factorial (i.e., N_(T)!) possiblesub-hypotheses that may be evaluated. Each sub-hypothesis corresponds toa specific assignment of each transmit antenna to a respective terminal.Each sub-hypothesis may thus be represented with a vector ofpost-processed SNRS, which may be expressed as:γ _(sub-hyp)={γ_(1,a),γ_(2,b), . . . ,γ_(N) _(T) _(,r)},where γ_(i,j) is the post-processed SNR for the i-th transmit antenna tothe j-th terminal, and the subscripts {a, b, . . . and r} identify thespecific terminals in the transmit antenna/terminal pairings for thesub-hypothesis.

Each sub-hypothesis is further associated with a performance metric,R_(sub-hyp), which may be a function of various factors. For example, aperformance metric based on the post-processed SNRs may be expressed as:R _(sub-hyp) =f(γ _(sub-hyp)),where f(•) is a particular positive real function of the argument(s)within the parenthesis.

Various functions may be used to formulate the performance metric. Inone embodiment, a function of the achievable throughput for all N_(T)transmit antennas for the sub-hypothesis may be used, which may beexpressed as:

$\begin{matrix}{{{f\left( {\underset{\_}{\gamma}}_{{sub} - {hyp}} \right)} = {\sum\limits_{i = 1}^{N_{T}}r_{i}}},} & {{Eq}\mspace{14mu}(3)}\end{matrix}$where r_(i) is the throughput associated with the i-th transmit antennain the sub-hypothesis, and may be expressed as:r _(i) =c _(i)·log₂(1+γ_(i)),  Eq (4)where c_(i) is a positive constant that reflects the fraction of thetheoretical capacity achieved by the coding and modulation schemeselected for the data stream transmitted on the i-th transmit antenna,and γ_(i) is the post-processed SNR for the i-th data stream.

The first antenna assignment scheme shown in FIG. 2 and described aboverepresents a specific scheme that evaluates all possible combinations ofassignments of transmit antennas to terminals. The total number ofpotential sub-hypotheses to be evaluated by the scheduler for eachhypothesis is N_(T)!, which may be large considering that a large numberof hypotheses may need to be evaluated. The first scheduling schemeperforms an exhaustive search to determine the sub-hypothesis thatprovides the “optimal” system performance, as quantified by theperformance metric used to select the best sub-hypothesis.

A number of techniques may be used to reduce the complexity of theprocessing to assign transmit antennas. One of these techniques isdescribed below, and others may also be implemented and are within thescope of the invention. These techniques may also provide high systemperformance while reducing the amount of processing required to assigntransmit antennas to terminals.

In a second antenna assignment scheme, a maximum—maximum (“max—max”)criterion is used to assign transmit antennas to the terminals in thehypothesis being evaluated. Using this max—max criterion, each transmitantenna is assigned to a particular terminal that achieves the best SNRfor the transmit antenna. The antenna assignment is performed for onetransmit antenna at a time.

FIG. 3 is a flow diagram of a process 300 to assign transmit antennas toterminals using the max—max criterion, in accordance with an embodimentof the invention. The processing shown in FIG. 3 is performed for aparticular hypothesis, which corresponds to a specific set of one ormore terminals. Initially, the maximum post-processed SNR in thehypothesis matrix Γ is determined, at step 312. This maximum SNRcorresponds to a specific transmit antenna/terminal pairing, and thetransmit antenna is assigned to this terminal, at step 314. Thistransmit antenna and terminal are then removed from the matrix Γ, andthe matrix is reduced to dimension (N_(T) −1)×(N_(T)−1) by removing boththe column corresponding to the transmit antenna and the rowcorresponding to the terminal just assigned, at step 316.

At step 318, a determination is made whether or not all transmitantennas in the hypothesis have been assigned. If all transmit antennashave been assigned, then the antenna assignments are provided, at step320, and the process terminates. Otherwise, the process returns to step312 and another transmit antenna is assigned in similar manner.

Once the antenna assignments have been made for a given hypothesismatrix Γ, the performance metric (e.g., the system throughput)corresponding to this hypothesis may be determined (e.g., based on theSNRs corresponding to the antenna assignments), as shown in equations(3) and (4). This performance metric is updated for each hypothesis.When all hypotheses have been evaluated, the best set of terminals andantenna assignments are selected for data transmission in the upcomingtransmission interval.

Table 1 shows an example matrix Γ of post-processed SNRs derived byterminals in a 4×4 MIMO system in which the base station includes fourtransmit antennas and each terminal includes four receive antennas. Forthe antenna assignment scheme based on the max—max criterion, the bestSNR (16 dB) in the original matrix is achieved by transmit antenna 3 andis assigned to terminal 1, as indicated by the shaded box in the thirdrow of the fourth column in the table. Transmit antenna 3 and terminal 1are then removed from the matrix. The best SNR (14 dB) in the reduced3×3 matrix is achieved by both transmit antennas 1 and 4, which arerespectively assigned to terminals 3 and 2. The remaining transmitantenna 2 is then assigned to terminal 4.

TABLE 1

Table 2 shows the antenna assignments using the max—max criterion forthe example matrix Γ shown in Table 1. For terminal 1, the best SNR (16dB) is achieved when processing the signal transmitted from transmitantenna 3. The best transmit antennas for the other terminals are alsoindicated in Table 2. The scheduler can use this information to selectthe proper coding and modulation scheme to employ for data transmission.

TABLE 2 Terminal Transmit Antenna SNR (dB) 1 3 16 2 4 14 3 1 14 4 2 10

The scheduling scheme described in FIGS. 2 and 3 represents a specificscheme that evaluates various hypotheses corresponding to variouspossible sets of active terminals desiring data transmission in theupcoming transmission interval. The total number of hypotheses to beevaluated by the scheduler can be quite large, even for a small numberof active terminals. In fact, the total number of hypotheses, N_(hyp),can be expressed as:

$\begin{matrix}{{N_{hyp} = {\begin{pmatrix}N_{U} \\N_{T}\end{pmatrix} = \frac{N_{U}!}{{\left( {N_{U} - N_{T}} \right)!}{N_{T}!}}}},} & {{Eq}\mspace{14mu}(5)}\end{matrix}$where N_(U) is the number of active terminals to be considered forscheduling. For example, if N_(U)=8 and N_(T)=4, then N_(hyp)=70. Anexhaustive search may be used to determine the particular hypothesis(and the particular antenna assignments) that provides the optimalsystem performance, as quantified by the performance metric used toselect the best hypothesis and antenna assignments.

Other scheduling schemes having reduced complexity may also beimplemented and are within the scope of the invention. One suchscheduling scheme is described below. These schemes may also providehigh system performance while reducing the amount of processing requiredto schedule terminals for data transmission.

In another scheduling scheme, active terminals are scheduled for datatransmission based on their priority. The priority of each terminal maybe derived based on one or more metrics (e.g., average throughput),system constraints and requirements (e.g., maximum latency), otherfactors, or a combination thereof, as described below. A list may bemaintained for all active terminals desiring data transmission in anupcoming transmission interval (which is also referred to as a “frame”).When a terminal desires data transmission, it is added to the list andits metrics are initialized (e.g., to zero). The metrics of eachterminal in the list are thereafter updated on each frame. Once aterminal no longer desires data transmission, it is removed from thelist.

For each frame, all or a subset of the terminals in the list may beconsidered for scheduling. The specific number of terminals to beconsidered may be based on various factors. In one embodiment, only theN_(T) highest priority terminals are selected for data transmission. Inanother embodiment, the highest N_(X) priority terminals in the list areconsidered for scheduling, where N_(X)>N_(T).

FIG. 4 is a flow diagram for a priority-based scheduling scheme 400whereby a set of N_(T) highest priority terminals is considered forscheduling, in accordance with an embodiment of the invention. At eachframe interval, the scheduler examines the priority for all activeterminals in the list and selects the set of N_(T) highest priorityterminals, at step 412. The remaining terminals in the list are notconsidered for scheduling. The channel estimates for each selectedterminal are then retrieved, at step 414. For example, thepost-processed SNRs for the selected terminals may be retrieved and usedto form the hypothesis matrix Γ.

The N_(T) transmit antennas are then assigned to the selected terminalsbased on the channel estimates and using any one of a number of antennaassignment schemes, at step 416. For example, the antenna assignmentsmay be based on an exhaustive search or the max—max criterion describedabove. In another antenna assignment scheme, the transmit antennas areassigned to the terminals such that their priorities are normalized asclose as possible, after the terminal metrics are updated.

The data rates and coding and modulation schemes for the terminals arethen determined based on the antenna assignments, at step 418. Thescheduled transmission interval and data rates may be reported to thescheduled terminals. The metrics of scheduled (and unscheduled)terminals in the list are updated to reflect the scheduled datatransmission (and non-transmission), and system metrics are alsoupdated, at step 420.

Various metrics and factors may be used to determine the priority of theactive terminals. In an embodiment, a “score” may be maintained for eachterminal in the list and for each metric to be used for scheduling. Inone embodiment, a score indicative of an average throughput over aparticular averaging time interval is maintained for each activeterminal. In one implementation, the score φ_(n)(k) for terminal n atframe k is computed as a linear average throughput achieved over sometime interval, and can be expressed as:

$\begin{matrix}{{{\phi_{n}(k)} = {\frac{1}{K}{\sum\limits_{i = {k - K + 1}}^{k}{{r_{n}(i)}/r_{\max}}}}},} & {{Eq}\mspace{14mu}(6)}\end{matrix}$where r_(n)(i) is the realized data rate (in unit of bits/frame) forterminal n at frame i and may be computed as shown in equation (4).Typically, r_(n)(i) is bounded by a particular maximum achievable datarate, r_(max), and a particular minimum data rate (e.g., zero). Inanother implementation, the score φ_(n)(k) for terminal n in frame k isan exponential average throughput achieved over some time interval, andcan be expressed as:φ_(n)(k)=(1−α)·φ_(n)(k−1)+α·r _(n)(k)/r _(max),  Eq (7)where α is a time constant for the exponential averaging, with a largervalue for α corresponding to a longer averaging time interval.

When a terminal desires data transmission, it is added to the list andits score is initialized to zero. The score for each terminal in thelist is subsequently updated on each frame. Whenever a terminal is notscheduled for transmission in a frame, its data rate for the frame isset to zero (i.e., r_(n)(k)=0) and its score is updated accordingly. Ifa frame is received in error by a terminal, the terminal's effectivedata rate for that frame may be set to zero. The frame error may not beknown immediately (e.g., due to round trip delay of anacknowledgment/negative acknowledgment (Ack/Nak) scheme used for thedata transmission) but the score can be adjusted accordingly once thisinformation is available.

The priority for the active terminals may also be determined based inpart on system constraints and requirements. For example, if the maximumlatency for a particular terminal exceeds a threshold value, then theterminal may be elevated to a high priority.

Other factors may also be considered in determining the priority of theactive terminals. One such factor may be related to the type of data tobe transmitted to the terminals. Delay sensitive data may be associatedwith higher priority, and delay insensitive may be associated with lowerpriority. Retransmitted data due to decoding errors for a priortransmission may also be associated with higher priority since otherprocesses may be awaiting the retransmitted data. Another factor may berelated to the type of data service being provided for the terminals.Other factors may also be considered in determining priority and arewithin the scope of the invention.

The priority of a terminal may thus be a function of any combination of(1) the score maintained for the terminal for each metric to beconsidered, (2) other parameter values maintained for system constraintsand requirements, and (3) other factors. In one embodiment, the systemconstraints and requirements represent “hard” values (i.e., high or lowpriority, depending on whether or not the constraints and requirementshave been violated) and the scores represent “soft” values. For thisembodiment, terminals for which the system constraints and requirementshave not been met are immediately considered, along with other terminalsbased on their scores.

A priority-based scheduling scheme may be designed to achieve equalaverage throughput (i.e., equal QoS) for all terminals in the list. Inthis case, active terminals are prioritized based on their achievedaverage throughput, which may be determined as shown in equation (6) or(7). In this priority-based scheduling scheme, the scheduler uses thescores to prioritize terminals for assignment to the available transmitantennas. The scores of the terminals are updated based on theirassignments or non-assignments to transmit antennas. The activeterminals in the list may be prioritized such that the terminal with thelowest score is given the highest priority, and the terminal with thehighest score is conversely given the lowest priority. Other methods forranking terminals may also be used. The prioritization may also assignnon-uniform weighting factors to the terminal scores.

For a scheduling scheme in which terminals are selected and scheduledfor data transmission based on their priority, it is possible for poorterminal groupings to occur occasionally. A “poor” terminal set is onethat results in similar channel response matrices H_(k) which causesimilar and poor SNRs for all terminals on all transmit data streams asgiven in the hypothesis matrix Γ. This then results in low overallthroughput for each terminal in the set. When this happen, thepriorities of the terminals may not change substantially over severalframes. In this way, the scheduler may be stuck with this particularterminal set until the priorities change sufficiently to cause a changein membership in the set.

To avoid the above-described “clustering” effect, the scheduler can bedesigned to recognize this condition prior to assigning terminals to theavailable transmit antennas and/or detect the condition once it hasoccurred. A number of different techniques may be used to determine thedegree of linear dependence in the channel response matrices H_(k). Asimple method of detection is to apply a particular threshold on thehypothesis matrix Γ. If all SNRs are below this threshold, then theclustering condition is present. In the event that the clusteringcondition is detected, the scheduler can reorder the terminals (e.g., ina random manner) in an attempt to reduce the linear dependence in thehypothesis matrix. A shuffling scheme may also be devised to force thescheduler to select terminal sets that result in “good” hypothesismatrices (i.e., ones that have minimal amount of linear dependence).

Some of the scheduling schemes described above employ techniques toreduce the amount of processing required to select terminals and assigntransmit antennas to the selected terminals. These and other techniquesmay also be combined to derive other scheduling schemes, and this iswithin the scope of the invention. For example, the N_(X) highestpriority terminals may be considered for scheduling using any one of theschemes described above.

More complex scheduling schemes may also be devised that may be able toachieve throughput closer to optimum. These schemes may be required toevaluate a larger number of hypotheses and antenna assignments in orderto determine the best set of terminals and the best antenna assignments.Other scheduling schemes may also be designed to take advantage of thestatistical distribution of the data rates achieved by each terminal.This information may be useful in reducing the number of hypotheses tobe evaluated. In addition, for some applications, it may be possible tolearn which terminal groupings (i.e., hypotheses) work well by analyzingperformance over time. This information may then be stored, updated, andused by the scheduler in future scheduling intervals.

The techniques described above may be used schedule terminals for datatransmission using the MIMO mode, N-SIMO mode, and mixed-mode. Otherconsiderations may also be applicable for each of these operating modes,as described below.

MIMO Mode

In the MIMO mode, (up to) N_(T) independent data streams may besimultaneously transmitted by the base station from N_(T) transmitantennas and targeted to a single MIMO terminal with N_(R) receiveantennas (i.e., N_(T)×N_(R) MIMO), where N_(R)≧N_(T). The terminal mayuse spatial equalization (for a non-dispersive MIMO channel with a flatfrequency channel response) or space-time equalization (for a dispersiveMIMO channel with a frequency dependent channel response) to process andseparate the N_(T) transmitted data streams. The SNR of eachpost-processed data stream (i.e., after equalization) may be estimatedand sent back to the base station as CSI, which then uses theinformation to select the proper coding and modulation scheme to use oneach transmit antenna such that the target terminal is able to detecteach transmitted data stream at the desired level of performance.

If all data streams are transmitted to one terminal as is the case inthe MIMO mode, then the successive cancellation receiver processingtechnique may be used at the terminal to process N_(R) received signalsto recover N_(T) transmitted data streams. This technique successivelyprocesses the N_(R) received signals a number of times (or iterations)to recover the signals transmitted from the terminals, with onetransmitted signal being recovered for each iteration. For eachiteration, the technique performs linear or non-linear processing (i.e.,spatial or space-time equalization) on the N_(R) received signals torecover one of the transmitted signals, and cancels the interference dueto the recovered signal from the received signals to derive “modified”signals having the interference component removed.

The modified signals are then processed by the next iteration to recoveranother transmitted signal. By removing the interference due to eachrecovered signal from the received signals, the SNR improves for thetransmitted signals included in the modified signals but not yetrecovered. The improved SNR results in improved performance for theterminal as well as the system. In fact, under certain operatingconditions, the performance achievable with the use of successivecancellation receiver processing in combination with a minimum meansquare error (MMSE) spatial equalization is comparable to that with fullCSI processing. The successive cancellation receiver processingtechnique is described in further detail in U.S. patent application Ser.No. 09/854,235, entitled “METHOD AND APPARATUS FOR PROCESSING DATA IN AMULTIPLE-INPUT MULTIPLE-OUTPUT (MIMO) COMMUNICATION SYSTEM UTILIZINGCHANNEL STATE INFORMATION,” filed May 11, 2001, assigned to the assigneeof the present application and incorporated herein by reference.

In an embodiment, each MIMO terminal in the system estimates and sendsback N_(T) post-processed SNR values for the N_(T) transmit antennas.The SNRs from the active terminals may be evaluated by the scheduler todetermine which terminal to transmit to and when, and the proper codingand modulation scheme to use on a per transmit antenna basis for eachselected terminal.

MIMO terminals may be selected for data transmission based on aparticular performance metric formulated to achieve the desired systemgoals. The performance metric may be based on one or more functions andany number of parameters. Various functions may be used to formulate theperformance metric, such as the function of the achievable throughputfor the MIMO terminals, which is shown above in equations (3) and (4).

N-SIMO Mode

In the N-SIMO mode, (up to) N_(T) independent data streams may besimultaneously transmitted by the base station from the N_(T) transmitantennas and targeted to (up to) N_(T) different SIMO terminals. Tomaximize performance, the scheduler may consider a large number ofpossible terminal sets for data transmission. The scheduler thendetermines the best set of N_(T) terminals to transmit simultaneously ona given channel (i.e., time slot, code channel, frequency sub-channel,and so on). In a multiple access communication system, there aregenerally constraints on satisfying certain requirements on a perterminal basis, such as maximum latency or average data rate. In thiscase, the scheduler can be designed to select the best set of terminalssubject to these constraints.

In one implementation for the N-SIMO mode, the terminals use linearspatial equalization to process the receive signals, and thepost-processed SNR corresponding to each transmit antenna is provided tothe base station. The scheduler then uses the information to select theterminals for data transmission and to assign the transmit antennas tothe selected terminals.

In another implementation for the N-SIMO mode, the terminals usesuccessive cancellation receiver processing to process the receivesignal to achieved higher post-processed SNRs. With successivecancellation receiver processing, the post-processed SNRs for thetransmitted data streams depend on the order in which the data streamsare detected (i.e., demodulated and decoded). In some cases, aparticular SIMO terminal may not be able to cancel the interference froma given transmitted data stream intended for another terminal, since thecoding and modulation scheme used for this data stream was selectedbased on the other terminal's post-processed SNR. For example, thetransmitted data stream may be targeted for terminal u_(x) and coded andmodulated for proper detection at a (e.g., 10 dB) post-processed SNRachievable at the target terminal u_(x), but another terminal u_(y) mayreceive the same transmitted data stream at a worse post-processed SNRand is thus not able to properly detect the data stream. If the datastream intended for another terminal cannot be detected error free, thencancellation of the interference due to this data stream is notpossible. Successive cancellation receiver processing is viable when thepost-processed SNR corresponding to a transmitted data stream permitsreliable detection.

In order for the scheduler to take advantage of the improvement inpost-processed SNRs afforded by SIMO terminals using successivecancellation receiver processing, each such terminal can derive thepost-processed SNRs corresponding to different possible orderings ofdetection for the transmitted data streams. The N_(T) transmitted datastreams may be detected based on N_(T) factorial (i.e., N_(T)!) possibleorderings at a SIMO terminal, and each ordering is associated with N_(T)post-processed SNR values. Thus, N_(T)·N_(T)! SNR values may be reportedby each active terminal to the base station (e.g., if N_(T)=4, then 96SNR values may be reported by each SIMO terminal). The scheduler canthen use the information to select terminals for data transmission andto further assign transmit antennas to the selected terminals.

If successive cancellation receiver processing is used at the terminals,the scheduler can also consider the possible detection orderings foreach terminal. However, a large number of these ordering are typicallyinvalid because a particular terminal may not be able to properly detectdata streams transmitted to other terminals due to the lowerpost-processed SNRs achieved at this terminal for the undetectable datastreams.

As noted above, the transmit antennas may be assigned to the selectedterminals based on various schemes. In one antenna assignment scheme,the transmitted antennas are assigned to achieve high system performanceand based on the priority of the terminals.

Table 3 shows an example of the post-processed SNRs derived by eachterminal in a hypothesis being considered. For terminal 1, the best SNRis achieved when detecting the data stream transmitted from transmitantenna 3, as indicated by the shaded box in row 3, column 4 of thetable. The best transmit antennas for other terminals in the hypothesisare also indicated by the shading in the boxes.

TABLE 3

If each terminal identifies a different transmit antenna from which thebest post-processed SNR is detected, then the transmit antennas may beassigned to the terminals based on the their best post-processed SNRs.For the example shown in Table 3, terminal 1 may be assigned to transmitantenna 3, and terminal 2 may be assigned to transmit antenna 2.

If more than one terminal prefers the same transmit antenna, then thescheduler can determine the antenna assignments based on variouscriteria (e.g., fairness, performance metric, and others). For example,Table 3 indicates that the best post-processed SNRs for terminals 3 and4 occur for the data stream transmitted from the same transmitantenna 1. If the objective is to maximize throughput, then thescheduler may assign transmit antenna 1 to terminal 3 and transmitantenna 2 to terminal 4. However, if antennas are assigned to achievefairness, then transmit antenna 1 may be assigned to terminal 4 ifterminal 4 has higher priority than terminal 3.

Mixed-Mode

The techniques described above can be generalized to handle mixed SIMOand MIMO terminals. For example, if four transmit antennas are availableat the base station, then four independent data streams may betransmitted to a single 4×4 MIMO terminal, two 2×4 MIMO terminals, four1×4 SIMO terminals, one 2×4 MIMO terminal plus two 1×4 SIMO terminals,or any other combination of terminals designated to receive a total offour data streams. The scheduler can be designed to select the bestcombination of terminals based on the post-processed SNRs for varioushypothesized sets of terminals, where each hypothesized set may includea mixture of both MIMO and SIMO terminals.

Whenever mixed-mode traffic is supported, the use of successivecancellation receiver processing by the (e.g., MIMO) terminals placesadditional constraints on the scheduler due to the dependenciesintroduced. These constraints may result in more hypothesized sets beingevaluated, since in addition to considering different sets of terminalsthe scheduler must also consider demodulation of the data streams invarious orders by each terminal. The assignment of the transmit antennasand the selection of the coding and modulation schemes would then takeinto account these dependencies in order to achieve improvedperformance.

Transmit Antennas

The set of transmit antennas at a base station may be a physicallydistinct set of “apertures”, each of which may be used to directlytransmit a respective data stream. Each aperture may be formed by acollection of one or more antenna elements that are distributed in space(e.g., physically located at a single site or distributed over multiplesites). Alternatively, the antenna apertures may be preceded by one ormore (fixed) beam-forming matrices, with each matrix being used tosynthesize a different set of antenna beams from the set of apertures.In this case, the above description for the transmit antennas appliesanalogously to the transformed antenna beams.

A number of fixed beam-forming matrices may be defined in advance, andthe terminals may evaluate the post-processed SNRs for each of thepossible matrices (or sets of antenna beams) and send SNR vectors backto the base station. Different performance (i.e., post-processed SNRs)is typically achieved for different sets of transformed antenna beams,and this is reflected in the reported SNR vectors. The base station maythen perform scheduling and antenna assignment for each of the possiblebeam-forming matrices (using the reported SNR vectors), and select aparticular beam-forming matrix as well as a set of terminals and theirantenna assignments that achieve the best use of the availableresources.

The use of beam-forming matrices affords additional flexibility inscheduling terminals and may further provide improved performance. Asexamples, the following situations may be well suited for beam-formingtransformations:

-   -   Correlation in the MIMO channel is high so that the best        performance may be achieved with a small number of data streams.        However, transmitting with only a subset of the available        transmit antennas (and using only their associated transmit        amplifiers) results in a smaller total transmit power. A        transformation may be selected to use most or all of the        transmit antennas (and their amplifiers) for the data streams to        be sent. In this case, higher transmit power is achieved for the        transmitted data streams.    -   Physically dispersed terminals may be isolated somewhat by their        locations. In this case, the terminals may be served by a        standard FFT-type transformation of horizontally spaced        apertures into a set of beams pointed at different azimuths.

Performance

The techniques described herein may be viewed as a particular form ofspatial division multiple access (SDMA) wherein each transmit antenna inthe base station's antenna array is used to transmit a different datastream using channel state information (e.g., SNRs or some othersufficient parameter which determines the supportable data rate) derivedby the terminals in the coverage area. High performance is achieved onthe basis of the CSI, which is used in scheduling terminals andprocessing data.

The techniques described herein can provide improved system performance(e.g., higher throughput). Simulations have been performed to quantifythe possible system throughput with some of these techniques. In thesimulations, the channel response matrices H_(k) coupling the array oftransmit antennas and the receive antennas of the k-th terminal isassumed to be composed of equal-variance, zero-mean complex Gaussianrandom variables. The simulations were performed for the MIMO and N-SIMOmodes.

In the MIMO mode, four MIMO terminals (each with four receive antennas)are considered for each realization (e.g., each transmission interval)and the best terminal is selected and scheduled for data transmission.The scheduled terminal is transmitted four independent data streams anduses successive cancellation receiver processing (with MMSEequalization) to process the received signals and recover thetransmitted data streams. The average throughput for the scheduled MIMOterminals is recorded.

In the N-SIMO mode, four SIMO terminals, each with four receiveantennas, are considered for each realization. The post-processed SNRsfor each SIMO terminal are determined using MMSE linear spatialequalization (without successive cancellation receiver processing). Thetransmit antennas are assigned to the selected terminals based on themax—max criterion. The four scheduled terminals are transmitted fourindependent data streams and each terminal uses MMSE equalization toprocess the receive signal and recover its data stream. The throughputsfor each scheduled SIMO terminal are separately recorded, and theaverage throughput for all scheduled terminals is also recorded.

FIG. 9 shows the average throughput for a MIMO communication system withfour transmit antennas (i.e., N_(T)=4) and four receive antennas perterminal (i.e., N_(R)=4) for the MIMO and N-SIMO modes. The simulatedthroughput associated with each operating mode is provided as a functionof the average post-processed SNR. The average throughput for the MIMOmode is shown as plot 910, and the average throughput for the N-SIMOmode is shown as plot 912.

As shown in FIG. 9, the simulated throughput associated with the N-SIMOmode using the max—max criterion antenna assignment shows betterperformance than that achieved for the MIMO mode. In the MIMO mode, theMIMO terminals benefit by using successive cancellation receiverprocessing to achieve higher post-processed SNRs. In the SIMO mode, thescheduling schemes are able to exploit multi-user selection diversity toachieve improved performance (i.e., higher throughput) even though eachSIMO terminal uses linear spatial equalization. In fact, the multi-userdiversity provided in the N-SIMO mode results in an average downlinkthroughput that exceeds the throughput achieved by dividing atransmission interval into four equal-duration sub-slots and assigningeach MIMO terminal to a respective sub-slot.

The scheduling schemes used in the simulations for both operating modeswere not designed to provide proportionate fairness and some terminalswill observe higher average throughput than others. When a fairnesscriterion is imposed, the differences in throughput for the twooperating modes may diminish. Nevertheless, the ability to accommodateboth MIMO and N-SIMO terminals provides added flexibility to theprovisioning of wireless data services.

For simplicity, various aspects and embodiments of the invention havebeen described for a communication system in which (1) the number ofreceive antennas is equal to the number of transmit antennas (i.e.,N_(R)=N_(T)), and (2) one data stream is transmitted from each antennaat the base station. In this case, the number of transmission channelsis equal to the number of available spatial subchannels of the MIMOchannel. For a MIMO system that utilizes OFDM, multiple frequencysubchannels may be associated with each spatial subchannel, and thesefrequency subchannels may be assigned to terminals based on thetechniques described above. For a dispersive channel, a matrix H wouldrepresent a three-dimensional cube of channel response estimates foreach terminal.

Each scheduled terminal may also be equipped with more receive antennasthan the total number of data streams. Moreover, multiple terminals mayshare a given transmit antenna, and the sharing may be achieved via timedivision multiplexing (e.g., assigning different fractions of atransmission interval to different terminals), frequency divisionmultiplexing (e.g., assigning different frequency subchannels todifferent terminals), code division multiplexing (e.g., assigningdifferent orthogonal codes to different terminals), some othermultiplexing schemes, or any combinations of these schemes.

The scheduling schemes described herein select terminals and assignantennas for data transmission based on channel state information (e.g.,post-processed SNRs). The post-processed SNRs for the terminals aredependent on the particular transmit power level used for the datastreams transmitted from the base station. For simplicity, the sametransmit power level is assumed for all data streams (i.e., no powercontrol of the transmit power). However, by controlling the transmitpower for each antenna, the achievable SNRs may be adjusted. Forexample, by decreasing the transmit power for a particular transmitantenna via power control, the SNR associated with a data streamtransmitted from this antenna is reduced, the interference caused bythis data stream on other data streams would also be reduced, and otherdata streams may be able to achieve better SNRs. Thus, power control mayalso be used in conjunction with the scheduling schemes describedherein, and this is within the scope of the invention.

The scheduling of terminals based on priority is also described in U.S.Pat. No. 6,335,922 issued Jan. 1, 2002 to Tiedemann et al., entitled“METHOD AND APPARATUS FOR DETERMINING AVAILABLE TRANSMIT POWER IN AWIRELESS COMMUNICATION SYSTEM,” filed Sep. 29, 2000. Scheduling of datatransmission for the downlink is also described in U.S. patentapplication Ser. No. 08/798,951, entitled “METHOD AND APPARATUS FORFORWARD LINK RATE SCHEDULING,” filed Feb. 11, 1997. These applicationsare assigned to the assignee of the present invention and incorporatedherein by reference.

The scheduling schemes described herein incorporate a number of featuresand provide numerous advantages. Some of these features and advantagesare described below.

First, the scheduling schemes support various operating modes, includingmix-mode whereby any combination of SIMO and MIMO terminals may bescheduled for data transmission on the downlink. Each SIMO or MIMOterminal is associated with an SNR vector (i.e., one row in equation(2)). The scheduling schemes can evaluate any number of possiblecombinations of terminals for data transmission.

Second, the scheduling schemes provide a schedule for each transmissioninterval that includes a set of (optimal or near optimal) “mutuallycompatible” terminals based on their spatial signatures. Mutualcompatibility may be taken to mean coexistence of transmission on thesame channel and at the same time given specific constraints regardingterminals data rate requirements, transmit power, link margin,capability between SIMO and MIMO terminals, and possibly other factors.

Third, the scheduling schemes support variable data rate adaptationbased on the post-processed SNRs achieved at the terminals. Eachscheduled terminal may be informed when to expect data transmission, theassigned transmit antenna(s), and the data rate(s) to for the datatransmission (e.g., on a per transmit antenna basis).

Fourth, the scheduling schemes can be designed to consider sets ofterminals that have similar link margins. Terminals may be groupedaccording to their link margin properties. The scheduler may thenconsider combinations of terminals in the same “link margin” group whensearching for mutually compatible spatial signatures. This groupingaccording to link margin may improve the overall spectral efficiency ofthe scheduling schemes compared to that achieved by ignoring the linkmargins. Moreover, by scheduling terminals with similar link margins totransmit, downlink power control may be more easily exercised (e.g., onthe entire set of terminals) to improve overall spectral reuse. This maybe viewed as a combination of a downlink adaptive reuse scheduling incombination with SDMA for SIMO/MIMO. Scheduling based on link margins isdescribed in further detail in U.S. Pat. No. 6,493,331, issued Dec. 10,2002 to Walton, et al., entitled “METHOD AND APPARATUS FOR CONTROLLINGTRANSMISSIONS OF A COMMUNICATIONS SYSTEM,” filed Mar. 30, 2000, and U.S.patent application Ser. No. 09/848,937, entitled “METHOD AND APPARATUSFOR CONTROLLING UPLINK TRANSMISSIONS OF A WIRELESS COMMUNICATIONSYSTEM,” filed May 3, 2001, both assigned to the assignee of the presentapplication and incorporated herein by reference.

MIMO Communication System

FIG. 5 is a block diagram of base station 104 and terminals 106 withinMIMO communication system 100. At base station 104, a data source 512provides data (i.e., information bits) to a transmit (TX) data processor514. For each transmit antenna, TX data processor 514 (1) encodes thedata in accordance with a particular coding scheme, (2) interleaves(i.e., reorders) the encoded data based on a particular interleavingscheme, and (3) maps the interleaved bits into modulation symbols forone or more transmission channels selected for data transmission. Theencoding increases the reliability of the data transmission. Theinterleaving provides time diversity for the coded bits, permits thedata to be transmitted based on an average SNR for the transmit antenna,combats fading, and further removes correlation between coded bits usedto form each modulation symbol. The interleaving may further providefrequency diversity if the coded bits are transmitted over multiplefrequency subchannels. In an aspect, the coding and symbol mapping maybe performed based on control signals provided by a scheduler 534.

The encoding, interleaving, and signal mapping may be achieved based onvarious schemes. Some such schemes are described in the aforementionedU.S. patent application Ser. No. 09/854,235; U.S. patent applicationSer. No. 09/816,481, entitled “METHOD AND APPARATUS FOR UTILIZINGCHANNEL STATE INFORMATION IN A WIRELESS COMMUNICATION SYSTEM,” filedMar. 23, 2001; and U.S. patent application Ser. No. 09/776,073, entitled“CODING SCHEME FOR A WIRELESS COMMUNICATION SYSTEM,” filed Feb. 1, 2001,all assigned to the assignee of the present application and incorporatedherein by reference.

A TX MIMO processor 520 receives and demultiplexes the modulationsymbols from TX data processor 514 and provides a stream of modulationsymbols for each transmission channel (e.g., each transmit antenna), onemodulation symbol per time slot. TX MIMO processor 520 may furtherprecondition the modulation symbols for each selected transmissionchannel if full CSI (e.g., the channel response matrix H) is available.MIMO and full-CSI processing is described in further detail in U.S.patent application Ser. No. 09/532,492, entitled “HIGH EFFICIENCY, HIGHPERFORMANCE COMMUNICATIONS SYSTEM EMPLOYING MULTI-CARRIER MODULATION,”filed Mar. 22, 2000, assigned to the assignee of the present applicationand incorporated herein by reference.

If OFDM is not employed, TX MIMO processor 520 provides a stream ofmodulation symbols for each antenna used for data transmission. And ifOFDM is employed, TX MIMO processor 520 provides a stream of modulationsymbol vectors for each antenna used for data transmission. And iffull-CSI processing is performed, TX MIMO processor 520 provides astream of preconditioned modulation symbols or preconditioned modulationsymbol vectors for each antenna used for data transmission. Each streamis then received and modulated by a respective modulator (MOD) 522 andtransmitted via an associated antenna 524.

At each scheduled terminal 106, a number of receive antennas 552 receivethe transmitted signals, and each receive antenna provides a receivedsignal to a respective demodulator (DEMOD) 554. Each demodulator (orfront-end unit) 554 performs processing complementary to that performedat modulator 522. The modulation symbols from all demodulators 554 arethen provided to a receive (RX) MIMO/data processor 556 and processed torecover one or more data streams transmitted for the terminal. RXMIMO/data processor 556 performs processing complementary to thatperformed by TX data processor 514 and TX MIMO processor 520 andprovides decoded data to a data sink 560. The processing by terminal 106is described in further detail in the aforementioned U.S. patentapplication Ser. Nos. 09/854,235 and 09/776,073.

At each active terminal 106, RX MIMO/data processor 556 furtherestimates the link conditions and provides CSI (e.g., post-processedSNRs or channel gain estimates). A TX data processor 562 then receivesand processes the CSI, and provides processed data indicative of the CSIto one or more modulators 554. Modulator(s) 554 further condition theprocessed data and transmit the CSI back to base station 104 via areverse channel. The CSI may be reported by the terminal using varioussignaling techniques (e.g., in full, differentially, or a combinationthereof), as described in the aforementioned U.S. patent applicationSer. No. 09/816,481.

At base station 104, the transmitted feedback signal is received byantennas 524, demodulated by demodulators 522, and provided to a RXdata/MIMO processor 532. RX data/MIMO processor 532 performs processingcomplementary to that performed by TX data processor 562 and recoversthe reported CSI, which is then provided to scheduler 534.

Scheduler 534 uses the reported CSI to perform a number of functionssuch as (1) selecting the set of best terminals for data transmission,(2) assigning the available transmit antennas to the selected terminals,and (3) determining the coding and modulation scheme to be used for eachassigned transmit antenna. Scheduler 534 may schedule terminals toachieve high throughput or based on some other performance criteria ormetrics, as described above. In FIG. 5, scheduler 534 is shown as beingimplemented within base station 104. In other implementation, scheduler534 may be implemented within some other element of communication system100 (e.g., a base station controller that couples to and interacts witha number of base stations).

FIG. 6 is a block diagram of an embodiment of a base station 104 xcapable of processing data for transmission to the terminals based onCSI available to the base station (e.g., as reported by the terminals).Base station 104 x is one embodiment of the transmitter portion of basestation 104 in FIG. 5. Base station 104 x includes (1) a TX dataprocessor 514 x that receives and processes information bits to providemodulation symbols and (2) a TX MIMO processor 520 x that demultiplexesthe modulation symbols for the N_(T) transmit antennas.

In the specific embodiment shown in FIG. 6, TX data processor 514 xincludes a demultiplexer 608 coupled to a number of channel dataprocessors 610, one processor for each of the N_(C) transmissionchannels. Demultiplexer 608 receives and demultiplexes the aggregateinformation bits into a number of (up to N_(C)) data streams, one datastream for each of the transmission channels to be used for datatransmission. Each data stream is provided to a respective channel dataprocessor 610.

In the embodiment shown in FIG. 6, each channel data processor 610includes an encoder 612, a channel interleaver 614, and a symbol mappingelement 616. Encoder 612 receives and encodes the information bits inthe received data stream in accordance with a particular coding schemeto provide coded bits. Channel interleaver 614 interleaves the codedbits based on a particular interleaving scheme to provide timediversity. And symbol mapping element 616 maps the interleaved bits intomodulation symbols for the transmission channel used for transmittingthe data stream.

Pilot data (e.g., data of known pattern) may also be encoded andmultiplexed with the processed information bits. The processed pilotdata may be transmitted (e.g., in a time division multiplexed (TDM)manner) in all or a subset of the transmission channels used to transmitthe information bits. The pilot data may be used at the terminals toperform channel estimation.

As shown in FIG. 6, the data encoding, interleaving, and modulation (ora combination thereof) may be adjusted based on the available CSI (e.g.,as reported by the terminals). In one coding and modulation scheme,adaptive encoding is achieved by using a fixed base code (e.g., a rate1/3 Turbo code) and adjusting the puncturing to achieve the desired coderate, as supported by the SNR of the transmission channel used totransmit the data. For this scheme, the puncturing may be performedafter the channel interleaving. In another coding and modulation scheme,different coding schemes may be used based on the reported CSI. Forexample, each of the data streams may be coded with an independent code.With this scheme, a successive cancellation receiver processing schememay be used at the terminals to detect and decode the data streams toderive a more reliable estimate of the transmitted data streams, asdescribed in further detail below.

Symbol mapping element 616 can be designed to group sets of interleavedbits to form non-binary symbols, and to map each non-binary symbol intoa point in a signal constellation corresponding to a particularmodulation scheme (e.g., QPSK, M-PSK, M-QAM, or some other scheme)selected for the transmission channel. Each mapped signal pointcorresponds to a modulation symbol. The number of information bits thatmay be transmitted for each modulation symbol for a particular level ofperformance (e.g., one percent packet error rate (PER)) is dependent onthe SNR of the transmission channel. Thus, the coding and modulationscheme for each transmission channel may be selected based on theavailable CSI. The channel interleaving may also be adjusted based onthe available CSI.

The modulation symbols from TX data processor 514 x are provided to TXMIMO processor 520 x, which is one embodiment of TX MIMO processor 520in FIG. 5. Within TX MIMO processor 520 x, a demultiplexer 622 receives(up to) N_(C) modulation symbol streams from N_(C) channel dataprocessors 610 and demultiplexes the received modulation symbols into anumber of (N_(T)) modulation symbol streams, one stream for each antennaused to transmit the modulation symbols. Each modulation symbol streamis provided to a respective modulator 522. Each modulator 522 convertsthe modulation symbols into an analog signal, and further amplifies,filters, quadrature modulates, and upconverts the signal to generate amodulated signal suitable for transmission over the wireless link.

A transmitter design that implements OFDM is described in theaforementioned U.S. patent application Ser. Nos. 09/854,235, 09/816,481,09/776,073, and 09/532,492.

FIG. 7 is a block diagram of an embodiment of terminal 106 x capable ofimplementing various aspects and embodiments of the invention. Terminal106 x is one embodiment of the receive portion of terminals 106 athrough 106 n in FIG. 5 and implements the successive cancellationreceiver processing technique to receive and recover the transmittedsignals. The transmitted signals from (up to) N_(T) transmit antennasare received by each of N_(R) antennas 552 a through 552 r and routed toa respective demodulator (DEMOD) 554 (which is also referred to as afront-end processor). Each demodulator 554 conditions (e.g., filters andamplifies) a respective received signal, downconverts the conditionedsignal to an intermediate frequency or baseband, and digitizes thedownconverted signal to provide samples. Each demodulator 554 mayfurther demodulate the samples with a received pilot to generate astream of received modulation symbols, which is provided to a RXMIMO/data processor 556 x.

In the embodiment shown in FIG. 7, RX MIMO/data processor 556 x (whichis one embodiment of RX MIMO/data processor 556 in FIG. 5) includes anumber of successive (i.e., cascaded) receiver processing stages 710,one stage for each of the transmitted data stream to be recovered byterminal 106 x. In one transmit processing scheme, one data stream istransmitted on each transmission channel assigned to terminal 106 x, andeach data stream is independently processed (e.g., with its own codingand modulation scheme) and transmitted from a respective transmitantenna. For this transmit processing scheme, the number of data streamsis equal to the number of assigned transmission channels, which is alsoequal to the number of transmit antennas assigned for data transmissionto terminal 106 x (which may be a subset of the available transmitantennas). For clarity, RX MIMO/data processor 556 x is described forthis transmit processing scheme.

Each receiver processing stage 710 (except for the last stage 710 n)includes a channel MIMO/data processor 720 coupled to an interferencecanceller 730, and the last stage 710 n includes only channel MIMO/dataprocessor 720 n. For the first receiver processing stage 710 a, channelMIMO/data processor 720 a receives and processes the N_(R) modulationsymbol streams from demodulators 554 a through 554 r to provide adecoded data stream for the first transmission channel (or the firsttransmitted signal). And for each of the second through last stages 710b through 710 n, channel MIMO/data processor 720 for that stage receivesand processes the N_(R) modified symbol streams from the interferencecanceller 720 in the preceding stage to derive a decoded data stream forthe transmission channel being processed by that stage. Each channelMIMO/data processor 720 further provides CSI (e.g., the SNR) for theassociated transmission channel.

For the first receiver processing stage 710 a, interference canceller730 a receives the N_(R) modulation symbol streams from all N_(R)demodulators 554. And for each of the second through second-to-laststages, interference canceller 730 receives the N_(R) modified symbolstreams from the interference canceller in the preceding stage. Eachinterference canceller 730 also receives the decoded data stream fromchannel MIMO/data processor 720 within the same stage, and performs theprocessing (e.g., coding, interleaving, modulation, channel response,and so on) to derive N_(R) remodulated symbol streams that are estimatesof the interference components of the received modulation symbol streamsdue to this decoded data stream. The remodulated symbol streams are thensubtracted from the received modulation symbol streams to derive N_(R)modified symbol streams that include all but the subtracted (i.e.,canceled) interference components. The N_(R) modified symbol streams arethen provided to the next stage.

In FIG. 7, a controller 740 is shown coupled to RX MIMO/data processor556 x and may be used to direct various steps in the successivecancellation receiver processing performed by processor 556 x.

FIG. 7 shows a receiver structure that may be used in a straightforwardmanner when each data stream is transmitted over a respective transmitantenna (i.e., one data stream corresponding to each transmittedsignal). In this case, each receiver processing stage 710 may beoperated to recover one of the transmitted signals and provide thedecoded data stream corresponding to the recovered transmitted signal.For some other transmit processing schemes, a data stream may betransmitted over multiple transmit antennas, frequency subchannels,and/or time intervals to provide spatial, frequency, and time diversity,respectively. For these schemes, the receiver processing initiallyderives a received modulation symbol stream for the transmitted signalon each transmit antenna of each frequency subchannel. Modulationsymbols for multiple transmit antennas, frequency subchannels, and/ortime intervals may then be combined in a complementary manner as thedemultiplexing performed at the base station. The stream of combinedmodulation symbols is then processed to provide the correspondingdecoded data stream.

FIG. 8A is a block diagram of an embodiment of channel MIMO/dataprocessor 720 x, which is one embodiment of channel MIMO/data processor720 in FIG. 7. In this embodiment, channel MIMO/data processor 720 xincludes a spatial/space-time processor 810, a CSI processor 812, aselector 814, a demodulation element 818, a de-interleaver 818, and adecoder 820.

Spatial/space-time processor 810 performs linear spatial processing onthe N_(R) received signals for a non-dispersive MIMO channel (i.e., withflat fading) or space-time processing on the N_(R) received signals fora dispersive MIMO channel (i.e., with frequency selective fading). Thespatial processing may be achieved using linear spatial processingtechniques such as a channel correlation matrix inversion (CCMI)technique, a minimum mean square error (MMSE) technique, and others.These techniques may be used to null out the undesired signals or tomaximize the received SNR of each of the constituent signals in thepresence of noise and interference from the other signals. Thespace-time processing may be achieved using linear space-time processingtechniques such as a MMSE linear equalizer (MMSE-LE), a decisionfeedback equalizer (DFE), a maximum-likelihood sequence estimator(MLSE), and others. The CCMI, MMSE, MMSE-LE, and DFE techniques aredescribed in further detail in the aforementioned U.S. patentapplication Ser. No. 09/854,235. The DFE and MLSE techniques are alsodescribed in further detail by S. L. Ariyavistakul et al. in a paperentitled “Optimum Space-Time Processors with Dispersive Interference:Unified Analysis and Required Filter Span,” IEEE Trans. onCommunication, Vol. 7, No. 7, July 1999, and incorporated herein byreference.

CSI processor 812 determines the CSI for each of the transmissionchannels used for data transmission. For example, CSI processor 812 mayestimate a noise covariance matrix based on the received pilot signalsand then compute the SNR of the k-th transmission channel used for thedata stream to be decoded. The SNR can be estimated similar toconventional pilot assisted single and multi-carrier systems, as isknown in the art. The SNR for all of the transmission channels used fordata transmission may comprise the CSI that is reported back to the basestation for this transmission channel. CSI processor 812 furtherprovides to selector 814 a control signal that identifies the particulardata stream to be recovered by this receiver processing stage.

Selector 814 receives a number of symbol streams from spatial/space-timeprocessor 810 and extracts the symbol stream corresponding to the datastream to be decoded, as indicated by the control signal from CSIprocessor 812. The extracted stream of modulation symbols is thenprovided to a demodulation element 814.

For the embodiment shown in FIG. 6 in which the data stream for eachtransmission channel is independently coded and modulated based on thechannel's SNR, the recovered modulation symbols for the selectedtransmission channel are demodulated in accordance with a demodulationscheme (e.g., M-PSK, M-QAM) that is complementary to the modulationscheme used for the transmission channel. The demodulated data fromdemodulation element 816 is then de-interleaved by a de-interleaver 818in a complementary manner to that performed by channel interleaver 614,and the de-interleaved data is further decoded by a decoder 820 in acomplementary manner to that performed by encoder 612. For example, aTurbo decoder or a Viterbi decoder may be used for decoder 820 if Turboor convolutional coding, respectively, is performed at the base station.The decoded data stream from decoder 820 represents an estimate of thetransmitted data stream being recovered.

FIG. 8B is a block diagram of an interference canceller 730 x, which isone embodiment of interference canceller 730 in FIG. 7. Withininterference canceller 730 x, the decoded data stream from the channelMIMO/data processor 720 within the same stage is re-encoded,interleaved, and re-modulated by a channel data processor 610× toprovide remodulated symbols, which are estimates of the modulationsymbols at the base station prior to the MIMO processing and channeldistortion. Channel data processor 610 x performs the same processing(e.g., encoding, interleaving, and modulation) as that performed at thebase station for the data stream. The remodulated symbols are thenprovided to a channel simulator 830, which processes the symbols withthe estimated channel response to provide estimates, î ^(k), of theinterference due the decoded data stream. The channel response estimatemay be derived based on the pilot and/or data transmitted by the basestation and in accordance with the techniques described in theaforementioned U.S. patent application Ser. No. 09/854,235.

The N_(R) elements in the interference vector î ^(k) correspond to thecomponent of the received signal at each of the N_(R) receive antennasdue to symbol stream transmitted on the k-th transmit antenna. Eachelement of the vector represents an estimated component due to thedecoded data stream in the corresponding received modulation symbolstream. These components are interference to the remaining (not yetdetected) transmitted signals in the N_(R) received modulation symbolstreams (i.e., the vector r ^(k)), and are subtracted (i.e., canceled)from the received signal vector r ^(k) by a summer 832 to provide amodified vector r ^(k+1) having the components from the decoded datastream removed. The modified vector r ^(k+1) is provided as the inputvector to the next receiver processing stage, as shown in FIG. 7.

Various aspects of the successive cancellation receiver processing aredescribed in further detail in the aforementioned U.S. patentapplication Ser. No. 09/854,235.

Receiver designs that do not employ the successive cancellation receiverprocessing technique may also be used to receive, process, and recoverthe transmitted data streams. Some such receiver designs are describedin the aforementioned U.S. patent application Ser. Nos. 09/776,073 and09/816,481, and U.S. patent application Ser. No. 09/532,492, entitled“HIGH EFFICIENCY, HIGH PERFORMANCE COMMUNICATIONS SYSTEM EMPLOYINGMULTI-CARRIER MODULATION,” filed Mar. 22, 2000, assigned to the assigneeof the present invention and incorporated herein by reference.

For simplicity, various aspects and embodiments of the invention havebeen described wherein the CSI comprises SNR. In general, the CSI maycomprise any type of information that is indicative of thecharacteristics of the communication link. Various types of informationmay be provided as CSI, some examples of which are described below.

In one embodiment, the CSI comprises signal-to-noise-plus-interferenceratio (SNR), which is derived as the ratio of the signal power over thenoise plus interference power. The SNR is typically estimated andprovided for each transmission channel used for data transmission (e.g.,each transmit data stream), although an aggregate SNR may also beprovided for a number of transmission channels. The SNR estimate may bequantized to a value having a particular number of bits. In oneembodiment, the SNR estimate is mapped to an SNR index, e.g., using alook-up table.

In another embodiment, the CSI comprises signal power and interferenceplus noise power. These two components may be separately derived andprovided for each transmission channel used for data transmission.

In yet another embodiment, the CSI comprises signal power, interferencepower, and noise power. These three components may be derived andprovided for each transmission channel used for data transmission.

In yet another embodiment, the CSI comprises signal-to-noise ratio plusa list of interference powers for each observable interference term.This information may be derived and provided for each transmissionchannel used for data transmission.

In yet another embodiment, the CSI comprises signal components in amatrix form (e.g., N_(T)×N_(R) complex entries for all transmit-receiveantenna pairs) and the noise plus interference components in matrix form(e.g., N_(T)×N_(R) complex entries). The base station may then properlycombine the signal components and the noise plus interference componentsfor the appropriate transmit-receive antenna pairs to derive the qualityfor each transmission channel used for data transmission (e.g., thepost-processed SNR for each transmitted data stream, as received at theterminals).

In yet another embodiment, the CSI comprises a data rate indicator foreach transmit data stream. The quality of a transmission channel to beused for data transmission may be determined initially (e.g., based onthe SNR estimated for the transmission channel) and a data ratecorresponding to the determined channel quality may then be identified(e.g., based on a look-up table). The identified data rate is indicativeof the maximum data rate that may be transmitted on the transmissionchannel for the required level of performance. The data rate is thenmapped to and represented by a data rate indicator (DRI), which can beefficiently coded. For example, if (up to) seven possible data rates aresupported by the base station for each transmit antenna, then a 3-bitvalue may be used to represent the DRI where, e.g., a zero may indicatea data rate of zero (i.e., don't use the transmit antenna) and 1 through7 may be used to indicate seven different data rates. In a typicalimplementation, the quality measurements (e.g., SNR estimates) aremapped directly to the DRI based on, e.g., a look-up table.

In another embodiment, the CSI comprises power control information foreach transmission channel. The power control information may include asingle bit for each transmission channel to indicate a request foreither more power or less power, or it may include multiple bits toindicate the magnitude of the change of power level requested. In thisembodiment, the base station may make use of the power controlinformation fed back from the terminals to adjust the data processingand/or the transmit power.

In yet another embodiment, the CSI comprises an indication of theparticular processing scheme to be used at the base station for eachtransmit data stream. In this embodiment, the indicator may identify theparticular coding scheme and the particular modulation scheme to be usedfor the transmit data stream such that the desired level of performanceis achieved.

In yet another embodiment, the CSI comprises a differential indicatorfor a particular measure of quality for a transmission channel.Initially, the SNR or DRI or some other quality measurement for thetransmission channel is determined and reported as a referencemeasurement value. Thereafter, monitoring of the quality of thetransmission channel continues, and the difference between the lastreported measurement and the current measurement is determined. Thedifference may then be quantized to one or more bits, and the quantizeddifference is mapped to and represented by the differential indicator,which is then reported. The differential indicator may indicate toincrease or decrease the last reported measurement by a particular stepsize (or to maintain the last reported measurement). For example, thedifferential indicator may indicate that (1) the observed SNR for aparticular transmission channel has increased or decreased by aparticular step size, or (2) the data rate should be adjusted by aparticular amount, or some other change. The reference measurement maybe transmitted periodically to ensure that errors in the differentialindicators and/or erroneous reception of these indicators do notaccumulate.

Other forms of CSI may also be used and are within the scope of theinvention. In general, the CSI includes sufficient information inwhatever form that may be used to adjust the processing at the basestation such that the desired level of performance is achieved for thetransmitted data streams.

The CSI may be derived based on the signals transmitted from the basestation and received at the terminals. In an embodiment, the CSI isderived based on a pilot reference included in the transmitted signals.Alternatively or additionally, the CSI may be derived based on the dataincluded in the transmitted signals.

In yet another embodiment, the CSI comprises one or more signalstransmitted on the uplink from the terminals to the base station. Insome systems, a degree of correlation may exist between the uplink anddownlink (e.g. time division duplexed (TDD) systems where the uplink anddownlink share the same band in a time division multiplexed manner). Inthese systems, the quality of the downlink may be estimated (to arequisite degree of accuracy) based on the quality of the uplink, whichmay be estimated based on signals (e.g., pilot signals) transmitted fromthe terminals. The pilot signals would then represent a means for whichthe base station could estimate the CSI as observed at the terminals.

The signal quality may be estimated at the terminals based on varioustechniques. Some of these techniques are described in the followingpatents, which are assigned to the assignee of the present applicationand incorporated herein by reference:

-   -   U.S. Pat. No. 5,799,005, entitled “SYSTEM AND METHOD FOR        DETERMINING RECEIVED PILOT POWER AND PATH LOSS IN A CDMA        COMMUNICATION SYSTEM,” issued Aug. 25, 1998,    -   U.S. Pat. No. 5,903,554, entitled “METHOD AND APPARATUS FOR        MEASURING LINK QUALITY IN A SPREAD SPECTRUM COMMUNICATION        SYSTEM,” issued May 11, 1999,    -   U.S. Pat. Nos. 5,056,109, and 5,265,119, both entitled “METHOD        AND APPARATUS FOR CONTROLLING TRANSMISSION POWER IN A CDMA        CELLULAR MOBILE TELEPHONE SYSTEM,” respectively issued Oct. 8,        1991 and Nov. 23, 1993, and    -   U.S. Pat. No. 6,097,972, entitled “METHOD AND APPARATUS FOR        PROCESSING POWER CONTROL SIGNALS IN CDMA MOBILE TELEPHONE        SYSTEM,” issued Aug. 1, 2000.

Methods for estimating a single transmission channel based on a pilotsignal or a data transmission may also be found in a number of papersavailable in the art. One such channel estimation method is described byF. Ling in a paper entitled “Optimal Reception, Performance Bound, andCutoff-Rate Analysis of References-Assisted Coherent CDMA Communicationswith Applications,” IEEE Transaction On Communication, October 1999.

Various types of information for CSI and various CSI reportingmechanisms are also described in U.S. Pat. No. 6,574,211, issued Jun. 3,2003 to Padovani et al., entitled “METHOD AND APPARATUS FOR HIGH RATEPACKET DATA TRANSMISSION,” filed Nov. 3, 1997, assigned to the assigneeof the present application, and in “TIE/EIA/1S-856 cdma2000 High RatePacket Data Air Interface Specification”, both of which are incorporatedherein by reference.

The CSI may be reported back to the base station using various CSItransmission schemes. For example, the CSI may be sent in full,differentially, or a combination thereof. In one embodiment, CSI isreported periodically, and differential updates are sent based on theprior transmitted CSI. In another embodiment, the CSI is sent only whenthere is a change (e.g., if the change exceeds a particular threshold),which may lower the effective rate of the feedback channel. As anexample, the SNRs may be sent back (e.g., differentially) only when theychange. For an OFDM system (with or without MIMO), correlation in thefrequency domain may be exploited to permit reduction in the amount ofCSI to be fed back. As an example for an OFDM system, if the SNRcorresponding to a particular spatial subchannel for NM frequencysubchannels is the same, the SNR and the first and last frequencysubchannels for which this condition is true may be reported. Othercompression and feedback channel error recovery techniques to reduce theamount of data to be fed back for CSI may also be used and are withinthe scope of the invention.

The elements of the base station and terminals may be implemented withone or more digital signal processors (DSP), application specificintegrated circuits (ASIC), processors, microprocessors, controllers,microcontrollers, field programmable gate arrays (FPGA), programmablelogic devices, other electronic units, or any combination thereof. Someof the functions and processing described herein may also be implementedwith software executed on a processor.

Certain aspects of the invention may be implemented with a combinationof software and hardware. For example, the processing to schedule (i.e.,select terminals and assign transmit antennas) may be performed based onprogram codes executed on a processor (scheduler 534 in FIG. 5).

Headings are included herein for reference and to aid in locatingcertain sections. These heading are not intended to limit the scope ofthe concepts described therein under, and these concepts may haveapplicability in other sections throughout the entire specification.

The previous description of the disclosed embodiments is provided toenable any person skilled in the art to make or use the presentinvention. Various modifications to these embodiments will be readilyapparent to those skilled in the art, and the generic principles definedherein may be applied to other embodiments without departing from thespirit or scope of the invention. Thus, the present invention is notintended to be limited to the embodiments shown herein but is to beaccorded the widest scope consistent with the principles and novelfeatures disclosed herein.

1. A method for scheduling data transmission in a wireless communicationsystem, the method comprising: identifying one or more sets ofterminals, each set including one or more terminals and corresponding toa hypothesis to be evaluated based on one or more criteria; assigning aplurality of transmit antennas to the one or more terminals in each set;evaluating performance of each hypothesis based on channel stateinformation (CSI) associated with each terminal, the CSI beingindicative of channel characteristics between the respective terminaland the corresponding transmit antennas; and selecting at least one setof terminals to receive data transmission based, at least in part, onthe performance of each hypothesis.
 2. The method of claim 1 furthercomprising: scheduling data transmission to the one or more terminals inthe selected set.
 3. The method of claim 1 wherein each hypothesiscomprises a plurality of sub-hypotheses each of which corresponding toone or more specific assignments of the transmit antennas to the one ormore terminals in the respective set and wherein the performance of eachhypothesis is evaluated based on performance of the correspondingsub-hypotheses.
 4. The method of claim 1, wherein the CSI for eachterminal comprises signal-to-noise-plus-interference ratio (SNR)estimates derived at the respective terminal based on signalstransmitted from the transmit antennas.
 5. The method of claim 1,wherein the evaluating includes: computing a performance metric for eachhypothesis as a function of throughput achievable by each terminal inthe respective set.
 6. The method of claim 1, wherein the plurality oftransmit antennas are assigned to the one or more terminals in each setbased on priority of terminals in the set.
 7. The method of claim 6,wherein the priority of each terminal is determined based on one or morefactors including quality of service (QoS) associated with therespective terminal.
 8. An apparatus for managing data transmission in awireless communication system, the apparatus comprising: means foridentifying one or more sets of terminals, each set including one ormore terminals and corresponding to a hypothesis to be evaluated basedon one or more criteria; means for assigning a plurality of transmitantennas to the one or more terminals in each set; means for evaluatingperformance of each hypothesis based on channel state information (CSI)associated with each terminal, the CSI being indicative of channelcharacteristics between the respective terminal and the correspondingtransmit antennas; and means for selecting at least one set of terminalsto receive data transmission based, at least in part, on the performanceof each hypothesis.
 9. The apparatus of claim 8 further comprising:means for scheduling data transmission to the one or more terminals inthe selected set.
 10. The apparatus of claim 8 wherein each hypothesiscomprises a plurality of sub-hypotheses each of which corresponding toone or more specific assignments of the transmit antennas to the one ormore terminals in the respective set and wherein the performance of eachhypothesis is evaluated based on performance of the correspondingsub-hypotheses.
 11. The apparatus of claim 8, wherein the CSI for eachterminal comprises signal-to-noise-plus-interference ratio (SNR)estimates derived at the respective terminal based on signalstransmitted from the transmit antennas.
 12. The apparatus of claim 8,wherein means for evaluating includes: means for computing a performancemetric for each hypothesis as a function of throughput achievable byeach terminal in the respective set.
 13. The apparatus of claim 8,wherein the plurality of transmit antennas are assigned to the one ormore terminals in each set based on priority of terminals in the set.14. The apparatus of claim 13, wherein the priority of each terminal isdetermined based on one or more factors including quality of service(QoS) associated with the respective terminal.